In most cases, sampling from an SI system is a stochastic process, and as a result, generated samples vary in quality. Developing a filtering process for generated material is a high priority concern, as low quality samples are undesirable when using a generative model in a production setting. Although measuring the log-likelihood of a sample can be useful as a proxy for quality, there are cases where log-likelihood significantly diverges from human perception. Theis et al. provide examples of generated images with high log-likelihood and extremely low quality [1]}. To the best of our knowledge, there are no pre-existing methods for ranking generated samples with respect to an arbitrary style.
